Thermophysical Properties of Short-Chain Branched Polyethylene

Polyethylene constitutes the major fraction of single-use plastics production. Current recycling efforts focus on mechanical recycling of plastics, often leading to performance losses. Recently, hydrocracking has come up as an attractive chemical recycling technology due to the availability of existing facilities that could accelerate industrial-scale applications. However, an understanding of how linear and branched polyethylene chains behave under hydrocracking conditions and the effect of the cracked products in the dynamics of the melt is still lacking.

In this work, we integrate molecular dynamics (MD) simulations and machine learning (ML) techniques to understand and predict thermophysical properties of linear and regularly spaced short chain branched polyethylene in the melt. We present two case studies in the context of chemical recycling where structure-property relationships can be used to accelerate circular polymer design and commercial polyolefin hydrocracking. Our results show how quantitative predictive models can aid the design of sustainable solutions for plastic innovation.